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- Hanqing LuSuggested from co-authorship
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Works264 from public data
- Unsupervised Feature Selection Using Nonnegative Spectral Analysis545
A new unsupervised learning algorithm, namely Nonnegative Discriminative Feature Selection (NDFS), which exploits the discriminative information and feature correlation simultaneously to select a better feature subset.
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- Learning Dual Convolutional Neural Networks for Low-Level Vision221
The proposed DualCNN consists of two parallel branches, which respectively recovers the structures and details in an end-to-end manner, and can generate the target signals according to the formation model for each particular application.
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- Weakly-supervised Semantic Guided Hashing for Social Image Retrieval192
This work proposes a novel Semantic Guided Hashing method coupled with binary matrix factorization to perform more effective nearest neighbor image search by simultaneously exploring the weakly-supervised rich community-contributed information and the underlying data structures.
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- BlockMix: Meta Regularization and Self-Calibrated Inference for Metric-Based Meta-Learning166
This work proposes new mechanisms to learn generalized and discriminative feature embeddings as well as improve the robustness of classifiers against prediction corruptions for meta-learning by integrating interpolation on the images and labels within metric learning.
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- Generalized Deep Transfer Networks for Knowledge Propagation in Heterogeneous Domains144
A novel generalized deep transfer networks (DTNs) capable of transferring label information across heterogeneous domains, textual domain to visual domain, and to share the labels between two domains are proposed, able to generate domain-specific and shared interdomain features.
- Participation-Contributed Temporal Dynamic Model for Group Activity Recognition140
A novel Participation-Contributed Temporal Dynamic Model (PC-TDM) to recognize group activity, which mainly consists of a "One" network and a " one to Key" network, which aims at modeling the individual dynamic of each person.
- M3Net: Multi-view Encoding, Matching, and Fusion for Few-shot Fine-grained Action Recognition134
M3Net is proposed, a matching-based framework for FS-FG action recognition, which incorporates multi-view encoding, multi- view matching, and multi-View fusion to facilitate embedding encoding, similarity matching,and decision making across multiple viewpoints.
- VRP-SAM: SAM with Visual Reference Prompt131
A novel Visual Reference Prompt encoder that empowers the Segment Any-thing Model (SAM) to utilize annotated reference images as prompts for segmentation, creating the VRP-SAM model, which achieves a breakthrough within the SAM framework by extending its versatility and applicabil-ity while preserving SAM's inherent strengths, thus enhancing user-friendliness.
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- Modeling Multimodal Clues in a Hybrid Deep Learning Framework for Video Classification123
A novel hybrid deep learning framework is introduced that integrates useful clues from multiple modalities, including static spatial appearance information, motion patterns within a short time window, audio information, as well as long-range temporal dynamics.
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- Continuously Growing Ultrathick CrN Coating to Achieve High Load-Bearing Capacity and Good Tribological Property121
The transmission electron microscopy observations and inverse Fourier-filtered images reveal a relaxation mechanism during the continuous growth of CrN coating, which can lead to a decrease in the residual stress when coating growth time exceeds 5 h, and the load-bearing capacity of coating is significantly increased.
- Deep Semantic Multimodal Hashing Network for Scalable Image-Text and Video-Text Retrievals118
The proposed DSMHN method is a generic and scalable deep hashing framework for both image-text and video-text retrievals, which can be flexibly integrated with different types of loss functions simultaneously.
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- Personalized Age Progression with Bi-Level Aging Dictionary Learning111
This work aims to automatically render aging faces in a personalized way by proposing a novel Bi-level Dictionary Learning based Personalized Age Progression (BDL-PAP) method, formulated to learn the aging dictionaries based on face pairs from neighboring age groups.
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- Deep Ordinal Hashing With Spatial Attention95
A novel deep ordinal hashing (DOH) method is proposed, which learns ordinal representations to generate ranking-based hash codes by leveraging the ranking structure of feature space from both local and global views.
- Supervised deep hashing for scalable face image retrieval90
This work proposes a novel supervised hashing method for scalable face image retrieval, i.e., Deep Hashing based on Classification and Quantization errors (DHCQ), by simultaneously learning feature representations of images, hash codes and classifiers.
- Face Super-Resolution Guided by 3D Facial Priors89
This paper proposes a novel face super-resolution method that explicitly incorporates 3D facial priors which grasp the sharp facial structures and is the first to explore 3D morphable knowledge based on the fusion of parametric descriptions of face attributes.
- How to Learn Item Representation for Cold-Start Multimedia Recommendation?88
A Multi-Task Pairwise Ranking (MTPR) framework for model training is proposed, which enforces the observed interactions ranking higher than the unobserved ones even if the item is assumed to be cold.
- Label distribution learning with label-specific features85
This paper proposes a novel LDL algorithm by leveraging label-specific features, where the common features for all labels and specific features for each label are simultaneously learned to enhance the LDL model.
- Learning Cluster-Wise Anchors for Multi-View Clustering84
An adaptive Cluster-wise Anchor learning based MVC method that supposes the prior cluster structure of target anchors by pre-defining a consensus cluster indicator matrix and can explore both inter-cluster diversity and intra-cluster consistency of anchors, and improve the subspace representation discrimination.
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- Social Anchor-Unit Graph Regularized Tensor Completion for Large-Scale Image Retagging81
This work proposes a novel Social anchor-Unit GrAph Regularized Tensor Completion (SUGAR-TC) method to efficiently refine the tags of social images, which is insensitive to the scale of data.
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- Entity-enhanced Adaptive Reconstruction Network for Weakly Supervised Referring Expression Grounding77
Qualitative results demonstrate that the proposed EARN can better handle the situation where multiple objects of a particular category are situated together and outperforms existing state-of-the-art methods.
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- Overlapping community detection based on node location analysis73
In this paper, a new overlapping community detection method based on node location analysis is proposed, using the PageRank algorithm to evaluate the node mass, and the community affiliation of nodes is determined based on their positions in the inherent peak-valley structure of the topology potential field.
- Image annotation using multi-correlation probabilistic matrix factorization73
A multi-correlation probabilistic matrix factorization (MPMF) algorithm for the correlation estimation of image-word correlation and the annotation words of each untagged or noisily tagged image can be predicted by reconstructing the image- word correlations with the both derived latent factors.
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- Tracking the evolution of overlapping communities in dynamic social networks65
A novel Dynamic Overlapping Community Evolution Tracking method to solve the three problems simultaneously with one single model, i.e. topology potential field, which can both accurately partition dynamic overlapping social networks and efficiently track all kinds of community evolution events.
- Visual Anomaly Detection via Partition Memory Bank Module and Error Estimation63
A novel Partition Memory Bank (PMB) module is proposed to effectively learn and store detailed features with semantic integrity of normal samples and improve the anomaly localization performance without increasing the cost.
- Show, Reward and Tell: Automatic Generation of Narrative Paragraph From Photo Stream by Adversarial Training58
This paper proposes a sequence-to-sequence modeling approach with reinforcement learning and adversarial training for the creation of narrative paragraph for a photo stream, and proposes a hierarchical recurrent neural network as story generator, optimized by reinforcement learning with rewards.
- Multimedia News Summarization in Search58
A novel approach of multimedia news summarization for searching results on the Internet is developed, which uncovers the underlying topics among query-related news information and threads the news events within each topic to generate a query- related brief overview.
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- Efficient Clothing Retrieval with Semantic-Preserving Visual Phrases58
Experiments and comparisons indicate that the SPVP significantly enhances the discriminative power of local features with a slight increase of memory usage or runtime consumption compared to the BOW model.
- Discriminative Deep Hashing for Scalable Face Image Retrieval57
A new Discriminative Deep Hashing network to learn discriminative and compact hash codes for large-scale face image retrieval and achieves superior performance compared with some state-of-the-art hashing methods.
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- Context Disentangling and Prototype Inheriting for Robust Visual Grounding54
A novel framework with context disentangling and prototype inheriting for robust visual grounding to handle both standard and open-vocabulary scenes is proposed and performance comparisons indicate that the method outperforms the state-of-the-art methods in both scenarios.
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- Semi-supervised local feature selection for data classification53
This paper proposes a new semi-supervised local feature selection method (S2LFS) allowing to select different feature subsets for different classes by learning the importance of features considering each class separately.
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- Spatial Structure Constraints for Weakly Supervised Semantic Segmentation51
A CAM-driven reconstruction module to directly reconstruct the input image from deep CAM features, which constrains the diffusion of last-layer object attention by preserving the coarse spatial structure of the image content, and an activation self-modulation module to refine CAMs with finer spatial structure details by enhancing regional consistency are proposed.
- Structure preserving non-negative matrix factorization for dimensionality reduction51
This paper proposes a novel dimensionality reduction algorithm, called Structure Preserving Non-negative Matrix Factorization (SPNMF), which is formulated as an optimization problem and solved by an effective iterative multiplicative update algorithm.
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- MLRank: Multi-correlation Learning to Rank for image annotation43
This paper forms image annotation as a Multi-correlation Learning to Rank (MLRank) problem, i.e., ranking the relevance of tags to an image considering the visual similarity and the semantic relevance, and obtains an explicit solution of the ranking model.
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- Causal Inference with Knowledge Distilling and Curriculum Learning for Unbiased VQA39
This work proposes a two-stage unbiased VQA approach that addresses the unbiased issue from a causal perspective and demonstrates the superior performance of the proposed method compared to the state-of-the-art (SotA) methods.
- RGBD Based Gaze Estimation via Multi-Task CNN38
This paper proposes a CNN-based multi-task learning framework to simultaneously refine depth images and predict gaze points to decompose gaze point estimation into eyeball pose, head pose, and 3D eye position estimation.
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- Learning Contrastive Self-Distillation for Ultra-Fine-Grained Visual Categorization Targeting Limited Samples37
Experimental results demonstrate that CSDNet outperforms current state-of-the-art Ultra-FGVC methods, emphasizing its powerful efficacy and adaptability in addressing Ultra-FGVC tasks.
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- Concurrence-Aware Long Short-Term Sub-Memories for Person-Person Action Recognition35
A novel Concurrent Long Short-Term Memories (Co-LSTM) is proposed to model the long-term inter-related dynamics between two interacting people on the bonding boxes covering people and is shown to have superior performance compared with the state-of-the-arts methods.
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- The 3rd Anti-UAV Workshop & Challenge: Methods and Results32
A brief summary of the 3rd Anti-UAV Workshop&Challenge is provided including brief introductions to the top three methods in each track including brief introduction to the bottom three methods.
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- Data-driven Meta-set Based Fine-Grained Visual Recognition26
This work proposes a data-driven meta-set based approach to deal with noisy web images for fine-grained recognition that can alleviate the harmful effects caused by out-of-distribution noise and properly exploit the in-dist distribution noisy samples for training.
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- DVF: Advancing Robust and Accurate Fine-Grained Image Retrieval with Retrieval Guidelines21
This paper designs a novel Dual Visual Filtering mechanism for the plain visual transformer, denoted as DVF, to capture subcategory-specific discrepancies and implements a discriminative model training strategy to improve the discriminability and generalization ability of DVF.
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- NuI-Go: Recursive Non-Local Encoder-Decoder Network for Retinal Image Non-Uniform Illumination Removal20
A non-uniform illumination removal network for retinal image, called NuI-Go, which consists of three Recursive Non-local Encoder-Decoder Residual Blocks (NEDRBs) for enhancing the degraded retinal images in a progressive manner is proposed.
- Extracting Useful Knowledge from Noisy Web Images via Data Purification for Fine-Grained Recognition19
This work investigates the noisy label problem and proposes a method that can specifically distinguish in- and out-of-distribution noisy samples and train the model on a less noisy web training set to achieve better robustness and performance.
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- RED-Nets: Redistribution Networks for Multi-View Classification18
A novel multi-view classification model termed Redistribution Networks for Multi-View Classification (RED-Nets) is proposed, which is able to jointly explore consistency and diversity in a flexible manner and is more effective than the state-of-the-art methods.
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- DiffusionVMR: Diffusion Model for Joint Video Moment Retrieval and Highlight Detection17
A novel framework, DiffusionVMR, is proposed to redefine the two tasks as a unified conditional denoising generation process by combining the diffusion model, inheriting the advantages of diffusion models that allow for iteratively refined results during inference, enhancing the boundary transition from coarse to fine.
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- VideoExpert: Augmented LLM for Temporal-Sensitive Video Understanding16
VideoExpert is a general-purpose MLLM suitable for several temporal-sensitive video tasks and integrates two parallel modules: the Temporal Expert and the Spatial Expert, which handles specially designed spatial tokens and language input, aiming to generate content-related responses.
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- Partially Common-Semantic Pursuit for RGB-D Object Recognition5
A novel supervised feature learning method for RGB-D object recognition, named Partially Common-Semantic Learning (PCSL), which jointly captures the complementary and consistency semantic information from RGB and depth modalities.
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- Bidirectional Multimodal Recurrent Neural Networks with Refined Visual Features for Image Captioning3
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- Time-IC: Empowering MLLM with Interleaved Context for Temporal-Sensitive Video Understanding–
Time-IC is proposed, a multimodal large language model that incorporates video context for improved understanding that integrates both internal context (timestamps, speech transcripts) and external context (titles, author descriptions), providing complementary semantic cues and high-level semantic priors.
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- Cross-modal Token Selection for Video Understanding–
This work introduces a novel transformer-based multi-modal architecture that outperforms existing state-of-the-art methods while significantly reducing the computational cost and extensive ablation studies showcase the benefits of the improved method over naive approaches.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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